Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 233-237).

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Data obtained from longitudinal surveys using complex multi-stage sampling designs contain cross-sectional dependencies among units caused by inherent hierarchies in the data, and within subject correlation arising due to repeated measurements. The statistical methods used for analyzing such data should account for stratification, clustering and unequal probability of selection as well as within-subject correlations due to repeated measurements. <p>The complex multi-stage design approach has been used in the longitudinal National Population Health Survey (NPHS). This on-going survey collects information on health determinants and outcomes in a sample of the general Canadian population. <p>This dissertation compares the model-based and design-based approaches used to determine the risk factors of asthma prevalence in the Canadian female population of the NPHS (marginal model). Weighted, unweighted and robust statistical methods were used to examine the risk factors of the incidence of asthma (event history analysis) and of recurrent asthma episodes (recurrent survival analysis). Missing data analysis was used to study the bias associated with incomplete data. To determine the risk factors of asthma prevalence, the Generalized Estimating Equations (GEE) approach was used for marginal modeling (model-based approach) followed by Taylor Linearization and bootstrap estimation of standard errors (design-based approach). The incidence of asthma (event history analysis) was estimated using weighted, unweighted and robust methods. Recurrent event history analysis was conducted using Anderson and Gill, Wei, Lin and Weissfeld (WLW) and Prentice, Williams and Peterson (PWP) approaches. To assess the presence of bias associated with missing data, the weighted GEE and pattern-mixture models were used.<p>The prevalence of asthma in the Canadian female population was 6.9% (6.1-7.7) at the end of Cycle 5. When comparing model-based and design- based approaches for asthma prevalence, design-based method provided unbiased estimates of standard errors. The overall incidence of asthma in this population, excluding those with asthma at baseline, was 10.5/1000/year (9.2-12.1). For the event history analysis, the robust method provided the most stable estimates and standard errors. <p>For recurrent event history, the WLW method provided stable standard error estimates. Finally, for the missing data approach, the pattern-mixture model produced the most stable standard errors <p>To conclude, design-based approaches should be preferred over model-based approaches for analyzing complex survey data, as the former provides the most unbiased parameter estimates and standard errors.

To get an optimal print result with rotogravure on improved newsprint, MF-paper, knowledge aboutwhat adjustment that should be done through the whole production is needed. This degree project investigatesthe reproduction’s effect on the final print result.A test print is done at Quebecor World Nordic Interprint´s regular print unit. The evaluation of theprint shows how important the use of a unit adjusted ICC-profile is for the print quality. A comparisonbetween the two separation methods GCR and UCR shows that Interprint´s present ICC-profile, usingUCR as separation method, gives less missing dots. When the cylinder that prints the black ink isengraving whit a slower speed than normal the result shows that the colour becomes a little darker, thedetails becomes clear but the difference is very small.

Data obtained from longitudinal surveys using complex multi-stage sampling designs contain cross-sectional dependencies among units caused by inherent hierarchies in the data, and within subject correlation arising due to repeated measurements. The statistical methods used for analyzing such data should account for stratification, clustering and unequal probability of selection as well as within-subject correlations due to repeated measurements. <p>The complex multi-stage design approach has been used in the longitudinal National Population Health Survey (NPHS). This on-going survey collects information on health determinants and outcomes in a sample of the general Canadian population. <p>This dissertation compares the model-based and design-based approaches used to determine the risk factors of asthma prevalence in the Canadian female population of the NPHS (marginal model). Weighted, unweighted and robust statistical methods were used to examine the risk factors of the incidence of asthma (event history analysis) and of recurrent asthma episodes (recurrent survival analysis). Missing data analysis was used to study the bias associated with incomplete data. To determine the risk factors of asthma prevalence, the Generalized Estimating Equations (GEE) approach was used for marginal modeling (model-based approach) followed by Taylor Linearization and bootstrap estimation of standard errors (design-based approach). The incidence of asthma (event history analysis) was estimated using weighted, unweighted and robust methods. Recurrent event history analysis was conducted using Anderson and Gill, Wei, Lin and Weissfeld (WLW) and Prentice, Williams and Peterson (PWP) approaches. To assess the presence of bias associated with missing data, the weighted GEE and pattern-mixture models were used.<p>The prevalence of asthma in the Canadian female population was 6.9% (6.1-7.7) at the end of Cycle 5. When comparing model-based and design- based approaches for asthma prevalence, design-based method provided unbiased estimates of standard errors. The overall incidence of asthma in this population, excluding those with asthma at baseline, was 10.5/1000/year (9.2-12.1). For the event history analysis, the robust method provided the most stable estimates and standard errors. <p>For recurrent event history, the WLW method provided stable standard error estimates. Finally, for the missing data approach, the pattern-mixture model produced the most stable standard errors <p>To conclude, design-based approaches should be preferred over model-based approaches for analyzing complex survey data, as the former provides the most unbiased parameter estimates and standard errors.

In this article, we propose an overview of missing data problem, introduce three missing data mechanisms and study general solutions to them when estimating a linear regression equation. When we have partly missing data, there are two common ways to solve this problem. One way is to ignore those records with missing values. Another method is to impute those observations being missed. Imputation methods arepreferred since they provide full datasets. We observed that there is not a general imputation solution in missing not at random (MNAR) mechanism. In order to check the performance of existing imputation methods in a regression model, a simulation study is set up. Listwise deletion, simple imputation and multiple imputation are selected into comparison which focuses on the effect on parameter estimates and standard errors. The simulation results illustrate that the listwise deletion provides reliable parameter estimates. Simple imputation performs better than multiple imputation in a model with a high determination coefficient. Multiple imputation,which offers a suitable solution for missing at random (MAR), is not valid for MNAR.

In the mid 1980s, national enterprises started to hire illegal foreign workers without the official introduction in Taiwan. Following with the soaring national economic growth, wage and income and the popular trend in stock, lottery and gambling games in 1985, workers were less willing to work. Industries, such as the 3K or 3D industry (dirty, dangerous and difficult), were less favored. In addition with the delaying participation of youth in job market due to the rising educational level, the supply for grassroots workers faced decreasing. Meanwhile, the national birth controlling policy also led to low birth rate, slow population growth and aging population, which triggered a serious labor shortage in Taiwan and enterprises also started calling for solving the labor shortage problem by introducing foreign workers. When Taiwanese government had first initiated the 14 major infrastructures project, the deadline of the project was kept postponing due to labor shortage. Therefore, the government set up a ¡§mechanism of workers demanding for14 major infrastructures project¡¨ by executive order, allowing entities to introduce foreign workers for the project. 3 years had gone, a ¡§mechanism for temporary labor shortage¡¨ was enforced. This mechanism allowed domestic industries to introduce foreign workers officially, announcing it can be applied to 15 jobs in 6 major industries. Through legislative procedure, the Employment Service Act had been passed on 3rd reading in 1992. Thus, a formal source of law can be found for introducing foreign workers. A riot started by Thai workers at Kaohsiung MRT in August 2005 not only revealed problems, such as foreign workers management and human rights, but also affected Taiwan¡¦s international reputation by large. Some foreign workers have received maltreatments, such as forced labor or involuntary labor. Moreover, the amount of foreign workers who are unaccounted for has kept increasing, too. In 2012, more than 30 thousand are missing. For researching the core problem, based on the 5 major frameworks of administration law, including the administrative principles, administrative organization, administrative authority, administrative remedy and administrative supervision, this study aims at examining the legal system of missing foreign workers prevention for national police to redress the deficiency of the legal system with researching by reference, historical analysis, comparative analysis and induction analysis. By comparing current management manners in Hong Kong, Singapore, Austria and Switzerland, I wish some valuable lessons can be learnt with more significant and concrete suggestions for related regulations to the missing foreign workers prevention and to protect human rights.

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Learning, inference, and prediction in the presence of missing data are pervasive problems in machine learning and statistical data analysis. This thesis focuses on the problems of collaborative prediction with non-random missing data and classification with missing features. We begin by presenting and elaborating on the theory of missing data due to Little and Rubin. We place a particular emphasis on the missing at random assumption in the multivariate setting with arbitrary patterns of missing data. We derive inference and prediction methods in the presence of random missing data for a variety of probabilistic models including finite mixture models, Dirichlet process mixture models, and factor analysis. Based on this foundation, we develop several novel models and inference procedures for both the collaborative prediction problem and the problem of classification with missing features. We develop models and methods for collaborative prediction with non-random missing data by combining standard models for complete data with models of the missing data process. Using a novel recommender system data set and experimental protocol, we show that each proposed method achieves a substantial increase in rating prediction performance compared to models that assume missing ratings are missing at random. We describe several strategies for classification with missing features including the use of generative classifiers, and the combination of standard discriminative classifiers with single imputation, multiple imputation, classification in subspaces, and an approach based on modifying the classifier input representation to include response indicators. Results on real and synthetic data sets show that in some cases performance gains over baseline methods can be achieved by methods that do not learn a detailed model of the feature space.